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Day-Ahead System Marginal Price Forecasting Using Artificial Neural Network and Similar-Days Information

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Abstract

Day-ahead system marginal price (SMP) forecasting constitutes essential information in the competitive energy market. Hence, this paper presents the development of a day-ahead SMP forecasting model via implementing an artificial neural network (ANN) algorithm. The accuracy of the ANN-based model is improved by including long-term historical data in addition to short-term historical data and by applying the k-fold cross-validation optimization algorithm. The selection of the short-term type input variable applies the Pearson correlation coefficient. Whereas the long-term type input variable is selected by applying the discrete Fréchet distance in conjunction with the information related to the season and type of the day to find the Similar-Days Index. In order to verify the model, the forecasted load and actual SMP for 15 years of historical data are used. The results indicate that the proposed model can forecast SMP with higher accuracy than the conventional forecasting model.

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Acknowledgements

This research was supported by Korea Electric Power Corporation (Grant number: R17XA05-37).

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Authors and Affiliations

  1. Department of Energy Systems Research, Ajou University, Suwon, South Korea

    Seongmun Oh & Jaesung Jung

  2. Electric Power and Energy Studies (EPES), Department of Electrical Engineering, Universitas Indonesia, Depok, Indonesia

    Fauzan Hanif Jufri

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  1. Fauzan Hanif Jufri
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  2. Seongmun Oh
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  3. Jaesung Jung
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Correspondence to Jaesung Jung.

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Jufri, F.H., Oh, S. & Jung, J. Day-Ahead System Marginal Price Forecasting Using Artificial Neural Network and Similar-Days Information. J. Electr. Eng. Technol. 14, 561–568 (2019). https://doi.org/10.1007/s42835-018-00058-w

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  • DOI: https://doi.org/10.1007/s42835-018-00058-w

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